하이퍼게임 이론을 위한 논리 기반 도메인 언어와 자동 합리화 절차

📝 Abstract

Differences in perception, information asymmetries, and bounded rationality lead game-theoretic players to derive a private, subjective view of the game that may diverge from the underlying “ground-truth” scenario and may be misaligned with other players’ interpretations. While typical game-theoretic assumptions often overlook such heterogeneity, hypergame theory provides the mathematical framework to reason about mismatched mental models. Although hypergames have recently gained traction in dynamic applications concerning uncertainty, their practical adoption in multi-agent system research has been hindered by the lack of a unifying, formal, and practical representation language, as well as scalable algorithms for managing complex hypergame structures and equilibria. Our work addresses this gap by introducing a declarative, logic-based domain-specific language for encoding hypergame structures and hypergame solution concepts. Leveraging answerset programming, we develop an automated pipeline for instantiating hypergame structures and running our novel hypergame rationalisation procedure, a mechanism for finding belief structures that justify seemingly irrational outcomes. The proposed language establishes a unifying formalism for hypergames and serves as a foundation for developing nuanced, belief-based heterogeneous reasoners, offering a verifiable context with logical guarantees. Together, these contributions establish the connection between hypergame theory, multi-agent systems, and strategic AI.

💡 Analysis

Differences in perception, information asymmetries, and bounded rationality lead game-theoretic players to derive a private, subjective view of the game that may diverge from the underlying “ground-truth” scenario and may be misaligned with other players’ interpretations. While typical game-theoretic assumptions often overlook such heterogeneity, hypergame theory provides the mathematical framework to reason about mismatched mental models. Although hypergames have recently gained traction in dynamic applications concerning uncertainty, their practical adoption in multi-agent system research has been hindered by the lack of a unifying, formal, and practical representation language, as well as scalable algorithms for managing complex hypergame structures and equilibria. Our work addresses this gap by introducing a declarative, logic-based domain-specific language for encoding hypergame structures and hypergame solution concepts. Leveraging answerset programming, we develop an automated pipeline for instantiating hypergame structures and running our novel hypergame rationalisation procedure, a mechanism for finding belief structures that justify seemingly irrational outcomes. The proposed language establishes a unifying formalism for hypergames and serves as a foundation for developing nuanced, belief-based heterogeneous reasoners, offering a verifiable context with logical guarantees. Together, these contributions establish the connection between hypergame theory, multi-agent systems, and strategic AI.

📄 Content

Hypergame Rationalisability: Solving Agent Misalignment In Strategic Play Vince Trencsenyi1[0009−0009−4560−7571] Royal Holloway University of London, Egham TW20 0EX, United Kingdom vince.trencsenyi@rhul.ac.uk Abstract. Differences in perception, information asymmetries, and bounded rationality lead game-theoretic players to derive a private, subjective view of the game that may diverge from the underlying “ground-truth” scenario and may be misaligned with other players’ inter- pretations. While typical game-theoretic assumptions often overlook such heterogeneity, hypergame theory provides the mathematical framework to reason about mismatched mental models. Although hypergames have recently gained traction in dynamic applications concerning uncertainty, their practical adoption in multi-agent system research has been hin- dered by the lack of a unifying, formal, and practical representation lan- guage, as well as scalable algorithms for managing complex hypergame structures and equilibria. Our work addresses this gap by introducing a declarative, logic-based domain-specific language for encoding hyper- game structures and hypergame solution concepts. Leveraging answer- set programming, we develop an automated pipeline for instantiating hypergame structures and running our novel hypergame rationalisation procedure, a mechanism for finding belief structures that justify seem- ingly irrational outcomes. The proposed language establishes a unifying formalism for hypergames and serves as a foundation for developing nu- anced, belief-based heterogeneous reasoners, offering a verifiable context with logical guarantees. Together, these contributions establish the con- nection between hypergame theory, multi-agent systems, and strategic AI. Keywords: Hypergame Theory · Multi-Agent-Based Simulations · Strate- gic AI. 1 Introduction Agents – human or artificial – rarely share a perfectly aligned understanding of the strategic situations they inhabit. While game theory provides a mathemat- ical framework for modelling and analysing decision-making in such strategic situations, typical game-theoretic models rely on simplifying assumptions about players’ rationality and interpretation of payoffs, omitting factors of heterogene- ity [21]. While these abstractions serve the tractability of game-theoretic models, they have also been subject to critique, stating that the extensive generalisa- tion omits the social context of the interaction, dehumanising the models and arXiv:2512.11942v1 [cs.AI] 12 Dec 2025 2 V. Trencsenyi thus creating a gap between theory and practical applications [16, 20]. Driven by similar motivations, Bennett [2] proposed hypergame theory as an exten- sion of game-theoretic concepts, relaxing the assumption of player homogeneity and allowing players to develop subjective games that capture their individual interpretations of the interaction based on their perceptions and beliefs. Agent-based applications provide a natural platform for scenarios that en- compass competitive or cooperative interactions within societies of actors [37]. Given that game theory offers a set of analytical tools for the same class of scenarios, Multi-Agent Systems (MAS) and game theory go hand-in-hand [15, 18, 19, 35]: MAS benefit from adopting game-theoretic interaction models, while game theory can rely on MAS for developing social simulations where strategic behaviour can be evaluated systematically. In this context, as agent misalignment remains a top-priority challenge to be solved [9, 11, 22, 23], developing realistic game-theoretic models as an interaction protocol for multi-agent based simu- lations has been receiving increasing research focus [24]. Consequently, while hypergame theory was initially developed primarily as an analytical framework, there has been a recent increasing tendency of hypergame-based multi-agent applications, most frequently targeting models of misaligned perceptions and deception [30]. In this work, we focus on MAS applications that directly integrate hyper- games into their interaction mechanisms. In particular, [29, 31] introduce a strongly conceptualised multi-agent system for replicating human reasoning in guessing games, where hypergames serve as a metaphor for players’ nested beliefs and provide the mechanism for recursive reasoning. We extend this work by deriving a formal, generalised framework grounded in logic and propose a novel approach for recovering hypergame structures that rationalise unexpected outcomes. Our key contributions are as follows: – We formalise a multi-agent centralised hypergames framework that captures bounded rationality and enables theory-of-mind-like recursive reasoning. – We express the system via a novel domain-specific language, formalising hypergame theory in a unified language grounded in logic. – We introduce hypergame rationalisability via strong and weak equilibria as novel concepts, contributing to the expressiveness and analytical capabilities of th

This content is AI-processed based on ArXiv data.